




"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		["image_processor", "tokenizer"]
				__UpperCamelCase         =		"ViltImageProcessor"
				__UpperCamelCase         =		("BertTokenizer", "BertTokenizerFast")





				def __init__(			self						,					_a=None						,					_a=None						,					**_a					):





							"""simple docstring"""
							lowerCamelCase      =   None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""						,					_a						,					)
										lowerCamelCase      =   kwargs.pop("""feature_extractor"""					)

							lowerCamelCase      =   image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""					)
							if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""					)

							super().__init__(_a						,					_a					)
							lowerCamelCase      =   self.image_processor





				def __call__(			self						,					_a						,					_a = None						,					_a = True						,					_a = False						,					_a = None						,					_a = None						,					_a = 0						,					_a = None						,					_a = None						,					_a = None						,					_a = False						,					_a = False						,					_a = False						,					_a = False						,					_a = True						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   self.tokenizer(
							    text=_a						,					add_special_tokens=_a						,					padding=_a						,					truncation=_a						,					max_length=_a						,					stride=_a						,					pad_to_multiple_of=_a						,					return_token_type_ids=_a						,					return_attention_mask=_a						,					return_overflowing_tokens=_a						,					return_special_tokens_mask=_a						,					return_offsets_mapping=_a						,					return_length=_a						,					verbose=_a						,					return_tensors=_a						,					**_a						,					)
							# add pixel_values + pixel_mask
							lowerCamelCase      =   self.image_processor(_a						,					return_tensors=_a					)
							encoding.update(_a					)

							return encoding





				def   _lowerCAmelCase							(			self						,					*_a						,					**_a					):





							"""simple docstring"""
							return self.tokenizer.batch_decode(*_a						,					**_a					)





				def   _lowerCAmelCase							(			self						,					*_a						,					**_a					):





							"""simple docstring"""
							return self.tokenizer.decode(*_a						,					**_a					)





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.tokenizer.model_input_names
							lowerCamelCase      =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							warnings.warn(
							    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""						,					_a						,					)
							return self.image_processor_class





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							warnings.warn(
							    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""						,					_a						,					)
							return self.image_processor









"""simple docstring"""




from __future__ import annotations

from decimal import Decimal

from numpy import array



def        a__					(							snake_case__							)		->   list[list[float]]:
			lowerCamelCase      =   Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(snake_case__							) == 2 and len(matrix[0]							) == 2 and len(matrix[1]							) == 2:
						# Calculate the determinant of the matrix
						lowerCamelCase      =   float(
						    d(matrix[0][0]							) * d(matrix[1][1]							) - d(matrix[1][0]							) * d(matrix[0][1]							)							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creates a copy of the matrix with swapped positions of the elements
						lowerCamelCase      =   [[0.0, 0.0], [0.0, 0.0]]
						lowerCamelCase ,  lowerCamelCase      =   matrix[1][1], matrix[0][0]
						lowerCamelCase ,  lowerCamelCase      =   -matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(snake_case__							)							) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(snake_case__							) == 3
			    and len(matrix[0]							) == 3
			    and len(matrix[1]							) == 3
			    and len(matrix[2]							) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						lowerCamelCase      =   float(
						    (
						        (d(matrix[0][0]							) * d(matrix[1][1]							) * d(matrix[2][2]							))
						        + (d(matrix[0][1]							) * d(matrix[1][2]							) * d(matrix[2][0]							))
						        + (d(matrix[0][2]							) * d(matrix[1][0]							) * d(matrix[2][1]							))
						    )
						    - (
						        (d(matrix[0][2]							) * d(matrix[1][1]							) * d(matrix[2][0]							))
						        + (d(matrix[0][1]							) * d(matrix[1][0]							) * d(matrix[2][2]							))
						        + (d(matrix[0][0]							) * d(matrix[1][2]							) * d(matrix[2][1]							))
						    )							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creating cofactor matrix
						lowerCamelCase      =   [
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						]
						lowerCamelCase      =   (d(matrix[1][1]							) * d(matrix[2][2]							)) - (
						    d(matrix[1][2]							) * d(matrix[2][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[1][0]							) * d(matrix[2][2]							)) - (d(matrix[1][2]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[1][0]							) * d(matrix[2][1]							)) - (
						    d(matrix[1][1]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][1]							) * d(matrix[2][2]							)) - (d(matrix[0][2]							) * d(matrix[2][1]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[2][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[2][1]							)) - (d(matrix[0][1]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][1]							) * d(matrix[1][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[1][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[1][2]							)) - (d(matrix[0][2]							) * d(matrix[1][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[1][1]							)) - (
						    d(matrix[0][1]							) * d(matrix[1][0]							)
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												lowerCamelCase      =   cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												inverse_matrix[i][j] /= d(snake_case__							)

        # Calculate the inverse of the matrix
						return [[float(d(snake_case__							)							) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""							)



